Classification of wheat grains by digital image analysis using statistical filters

The aim of pattern recognition by image analysis is to accurately determine the parameters of a given object so that recognition can be achieved. The recognition procedure finds its mathematical tools in signal processing. In the classification of wheats by image processing, spots and dust particles, considered 'noise' in the terminology of signal processing, may significantly decrease the performance of an edge detection algorithm. To remove a pulse noise and to extract the boundary of a geometry, rank-order filters have been proved to yield good results in the processing of an image. The developed software ImPro for Windows employs the median filtering technique to remove such impurities and to smooth out the surface of wheat grains. It also employs the range-order filtering technique to detect boundaries in the classification of wheat varieties. A captured image can be sent to the software directly from a frame grabber. Under standard lighting conditions, boundaries of grains are fit to ellipses, and feature parameters are determined. The parameters determined by fitting procedure, as well as the features determined by direct extraction, are used in the classification. Results show that the dimensionless rational features are useful in distinguishing most of the cultivars and advanced breeding lines tested. This revised version was published online in August 2006 with corrections to the Cover Date.     

Using Artificial Neural Network and Multiple Linear Regression for Predicting the Chlorophyll Concentration Index of Saint John’s Wort Leaves

This research investigates and compares artificial neural network and multiple linear regression for predicting the chlorophyll concentration index of Saint John’s wort leaves (Hypericum perforatum L.). Plants were fertilized with 0, 30, 60, 90, and 120 kg ha^?1 nitrogen [34% nitrogen ammonium nitrate (NH₄NO₃)]. Chlorophyll concentration index of each leaf was measured using SPAD meter. Afterwards, rgb (red, green, and blue color) values of all leaf images were determined by image processing. Values obtained were modeled using both multiple regression analysis and artificial neural networks. Using multiple regression analysis R² values were between 0.61 and 0.97. Coefficient of determination values (R²) using artificial neutral network values were found to be 0.99. Artificial neutral network modeling successfully described the relationship between actual chlorophyll concentration index values and predicted chlorophyll concentration index values.     

An Enterovirus-Like RNA Construct for Colon Cancer Suicide Gene Therapy

Background:

In gene therapy, the use of RNA molecules as therapeutic agents has shown advantages over plasmid DNA, including higher levels of safety. However, transient nature of RNA has been a major obstacle in application of RNA in gene therapy.

Methods:

Here, we used the internal ribosomal entry site of encephalomyocarditis virus and the 3’ non-translated region of Poliovirus to design an enterovirus-like RNA for the expression of a reporter gene (enhanced green fluorescent protein) and a suicide gene (thymidine kinase of herpes simplex virus). The expression of these genes was evaluated by flow cytometry and cytotoxicity assay in human colorectal adenocarcinoma cell line (SW480). We then armed RNA molecules with a target sequence for hsa-miR-143 to regulate their expression by microRNA (miRNA) mimics.

Results:

The results showed effective expression of both genes by Entrovirus-like RNA constructs. The data also showed that the restoration of hsa-miR-143 expression in SW480 leads to a significant translation repression of the introduced reporter and suicide genes.

Conclusion:

Collectively, our data suggest the potential use of Entrovirus-like RNA molecules in suicide gene therapy. Additionally, as a consequence of the possible downregulated miRNA expression in cancerous tissues, a decreased expression of gene therapy constructs armed with target sequences for such miRNA in cancer tissue is expected.Key Words: Thymidine kinase, Polio 3’NCR, miR-143